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Convergence Rate Analysis for Fixed-Point Iterations of Generalized Averaged Nonexpansive Operators - MaRDI portal

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Convergence Rate Analysis for Fixed-Point Iterations of Generalized Averaged Nonexpansive Operators

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Publication:6375280

DOI10.1007/S11784-022-00972-7arXiv2108.06714WikidataQ114221806 ScholiaQ114221806MaRDI QIDQ6375280

Author name not available (Why is that?)

Publication date: 15 August 2021

Abstract: We estimate convergence rates for fixed-point iterations of a class of nonlinear operators which are partially motivated from solving convex optimization problems. We introduce the notion of the generalized averaged nonexpansive (GAN) operator with a positive exponent, and provide a convergence rate analysis of the fixed-point iteration of the GAN operator. The proposed generalized averaged nonexpansiveness is weaker than the averaged nonexpansiveness while stronger than nonexpansiveness. We show that the fixed-point iteration of a GAN operator with a positive exponent converges to its fixed-point and estimate the local convergence rate (the convergence rate in terms of the distance between consecutive iterates) according to the range of the exponent. We prove that the fixed-point iteration of a GAN operator with a positive exponent strictly smaller than 1 can achieve an exponential global convergence rate (the convergence rate in terms of the distance between an iterate and the solution). Furthermore, we establish the global convergence rate of the fixed-point iteration of a GAN operator, depending on both the exponent of generalized averaged nonexpansiveness and the exponent of the Hddotextolder regularity, if the GAN operator is also Hddotextolder regular. We then apply the established theory to three types of convex optimization problems that appear often in data science to design fixed-point iterative algorithms for solving these optimization problems and to analyze their convergence properties.





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